Methods and apparatus for using black box data to analyze vehicular accidents

ABSTRACT

Disclosed are methods and apparatus for using black box data to analyze vehicular accidents. The methods include obtaining information from an event data recorder associated with a vehicle and using the data obtained therefrom in determining and analyzing the vehicular accident. Attributes to be analyzed include impact severity, change in velocity, and other desired parameters. Further disclosed are methods to securely communicate the downloaded black box information to a secure location for later analysis and processing.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to methods and apparatus for use ofso-called “black box” data, obtained from an event data recorder inconnection with analysis of a vehicular accident.

2. Description of the Related Art

Today, numerous “black box” technologies exist to provide auto crashinvestigators, insurance companies and legal counsel with significantinformation regarding a car accident. These technologies includeafter-market solutions that may include GPS capabilities, video capture,and storage of crash data. These technologies also include OEMsolutions. All of these “black box” solutions may be referred to as“event data recorder” or EDR technologies. For purposes of thisdisclosure, the terms EDR, “black box”, and CDR (“crash data retrieval”)will be used interchangeably.

Presently, black box data may be used by investigators, insurancecompanies, and others to help in the determination of the circumstancessurrounding a vehicle accident. Typically, this data consists ofpre-crash and post-crash information. The information may be used by aninvestigator or other interested party to aid in the analysis of theaccident and aid the determination of cause therefor based on theinformation. However, there are limitations in the use of such data, andpresently such data is only used by individuals having significantexpertise in accident reconstruction. Further, black box data is notcurrently used in connection with other computer-based vehicularaccident analysis tools.

SUMMARY OF THE INVENTION

In one aspect, the present invention includes a computer-implementedmethod for analyzing a collision between a first vehicle and a secondvehicle, including obtaining information from an event data recorderassociated with one of the vehicles; communicating the information to acomputer system; and utilizing the information in determining an impactseverity of at least one of the vehicles. In a further aspect, thepresent invention includes a computer-implemented method for assessingimpact severity of an accident involving a vehicle, including receivinginformation obtained from an event data recorder associated with thevehicle, and determining the impact severity using the information

Further aspects of the present invention include a computer-implementedmethod for analyzing information obtained from an event data recorder,including determining from a first data field obtained from the recorderwhether a passive restraint system was in use; applying a preselectedset of rules to the data field to determine a probability of interiorinteraction and occupant movement; and reporting these parameters.

Another aspect resides in a computer-implemented method for adjusting achange in velocity as determined by an event data recorder, includingobtaining the change in velocity, and modifying it by preselected valuesto obtain a low and high limit change in velocity. Such low and highlimits may then be used in connection with a Monte Carlo simulation.

Yet another aspect resides in a computer-implemented method fordetermining a calculated impact severity for a vehicle involved in anaccident, including receiving change in velocity data from an event datarecorder associated with the vehicle; determining a first calculation ofimpact severity using the change in velocity data; obtaining a secondcalculation of impact severity determined without the change in velocitydata; and combining the first and second calculations of impact severityto obtain the calculated impact severity. In certain embodiments, thefirst and/or second calculations may be weighted in accordance withpredetermined rules prior to combining.

A still further aspect resides in a computer-implemented method fordetermining impact severity, including receiving damage informationregarding two vehicles; accepting a description of pre-collisioninformation regarding the vehicles; determining a point of impact and aprincipal direction of force for each of the vehicles; receiving datafrom an event data recorder associated with said one of the vehicles;and determining impact severity for the second vehicle based on theinput and determined data.

Yet a further aspect resides in a computer-implemented method fordetermining impact severity of a collision between vehicles, includingreceiving information obtained from an event data recorder associatedwith one of the vehicles; calculating momentum vectors for the vehiclehaving the recorder using change in velocity information; anddetermining a momentum vector for the other second vehicle based on themomentum vectors of the first vehicle. In certain embodiments, thechange in velocity information may be adjusted prior to determination ofthe momentum vectors to account for any errors inherent in the EDR.

It is to be understood that all of the aspects described above andthroughout the specification may be implemented as computer-implementedmethods, and may further be resident on one or more computer-readablemedia, and/or resident on one or more data processing systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings.

FIG. 1 is a screen shot of an exemplary software program setting forthdata obtained from an event data recorder.

FIG. 2 is a drawing of equipment used to obtain black box informationfrom an event data recorder.

FIG. 3 is a block diagram of an impact configuration with a definedcollision plane of a first and second vehicle.

FIG. 4 is a block diagram of vehicle and collision geometry of acollision between a first vehicle and a second vehicle.

FIG. 5 is a block diagram illustrating the difference between a plasticcollision and an elastic collision.

FIG. 6 is a block diagram of a center of impact, C, of a collision of afirst vehicle and a second vehicle.

FIG. 7 is a flow chart of a Monte Carlo simulation process according tothe present invention.

FIG. 8A is a graphical representation of a coefficient of restitutionmodel in accordance with the present invention.

FIG. 8B is a graphical representation of a best fit analysis forunderestimation of a change in velocity as calculated by a vehicle EDR.

FIGS. 9A and 9B together are a flow chart of an example method inaccordance with the present invention.

FIG. 10 is a flow chart of an example seatbelt confirmation module inaccordance with the present invention.

FIG. 11 is a flow chart of an example human factors analysis module inaccordance with the present invention.

FIG. 12 is a flow chart of an example method combining various impactseverity determinations in accordance with the present invention.

FIG. 13 is a block diagram of an optimal accident configuration of anaccident involving a first vehicle and a second vehicle.

FIG. 14 is a flow chart of an example method in accordance with thepresent invention.

FIGS. 15A-C together are a flow chart of an example method for usingType 1 EDR data in accordance with the present invention.

FIGS. 16A-B together are a flow chart of an example method for usingType 2 EDR data in accordance with the present invention.

FIGS. 17A-B together are a flow chart of an example method for usingType 3 EDR data in accordance with the present invention.

The use of the same reference symbols in different drawings indicatessimilar or identical items.

DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

For purposes of this disclosure, the discussion of preferred embodimentswill focus on EDR technologies that are available as original equipmentto private passenger automobiles, trucks, vans and SUVs. This is becauseOEM technology has significant advantages over after-market solutions.These include: 1) the significant cost and coordination of eachinstallation of the technology is avoided; 2) unique or customizedtracking systems to recognize equipped vehicles are not required; 3) OEMtechnology has critical mass in the marketplace; and 4) OEM technologyhas a more established track record as valid, acceptable evidence inlitigated matters. However, it is to be understood that the presentinvention also applies equally to after market solutions.

As original equipment, the EDR may be implemented as the vehicle's SDM(sensing and diagnostic module), RCM (restraint control module) or othersimilar device that controls deployment of occupant protection systems.During a crash event, these systems will “wake up” or become activatedand, if the system determines that a crash is severe enough to warrantairbag deployment, for example, the airbag will be discharged. Dependingon the make and model of the vehicle, various elements of pre-crash andpost-crash data may be saved which may be subsequently harvested foranalysis of what actually happened during the accident event.

GM is the first manufacturer to allow equipment available to the generalpublic to access and retrieve EDR information. By the third quarter of2002, Ford may follow suit with several of its more popular models. Forthese reasons, the functionality of GM EDR system features will bediscussed as an example embodiment unless otherwise noted. In an exampleembodiment, a commercially available data retrieval system, such as theVETRONIX Crash Data Retrieval (CDR) system, may be used to access andretrieve the information. The example embodiment shown in FIG. 1 is ascreen shot from a software program commercially available fromVETRONIX, Santa Barbara, Calif. FIG. 1 provides a summary of data thatcan be downloaded from an OEM EDR on selected GM models in an exampleembodiment. Notable information contained in the data available includesthe following.

First, status of driver seatbelt usage at the time of the accident isrecorded. Although this indicator can be positive when the seatbeltharness is fastened, but not around the occupant, this information couldbe relevant to comparative or contributory negligence determinationswhen seatbelt use is at issue. This information can also be used todetermine occupant kinematics during the collision. Further, vehicleignition cycle count at the time of accident, as well as at the time the“black box” data was harvested, is available. Although current EDRtechnology does not capture the date of the accident, ignition cyclecounts (i.e., the number of times the ignition switch has been turned onand off) give an indication of the amount of usage a vehicle experiencesafter an accident but before the data is collected.

Also, longitudinal post-crash vehicle velocity changes (or Delta-V), inmph, at 10 milliseconds increments for the first 300 milliseconds aftera collision is recorded. This is crash pulse information that definesthe severity of the collision not only in magnitude, but also induration. Also, pre-crash information regarding vehicle speed (mph),engine speed (rpm), percent throttle and braking status (i.e., on oroff) at one second intervals for five seconds prior to impact isrecorded.

In another example embodiment, the Vetronix CDR system may harvest datafrom selected Ford models. Although pre-crash data will not be initiallyharvestable from these models, the information available will include,but not be limited to, the following: longitudinal or forward Delta-Vdata, lateral Delta-V data, driver seat belt status, and passenger seatbelt status. While discussed above with regard to GM and Ford modelcars, it is to be understood that EDR data may be obtained from, and themethods and system of the present invention may be used with, any makeor model vehicle having EDR technology.

In certain cases, when external factors are linked to the harvesteddata, insight into accident causation can be gleaned. For example, ifthe data shown in FIG. 1 was collected from an intersection collision,the information might be suggestive of a driver attempting to acceleratethrough an intersection before the traffic light turned red and decidingat the last moment to abort the attempt but failing to stop the vehicleprior to the collision. This scenario can be inferred because the “blackbox” data shows that the vehicle was in fact accelerating approximatelyfour seconds prior to the collision and that braking did not occur untilapproximately three seconds prior to the collision.

Clearly, EDR information provides objective and definitive informationregarding time, speed and distance factors as they relate to the causeof an accident. Such information is critical to accurate liabilitydeterminations.

After harvesting, “black box” data may be used in analysis of a vehiclecollision. In an example embodiment, the data may be imported into acomputer-based vehicle collision analysis system to provide greaterinsight into not only what happened to the vehicle from which the blackbox data was harvested, but also provide insight into what happened tothe other vehicle and its occupants.

In one embodiment after the data is harvested, it can be transmittedelectronically from the retrieval system to a central location. Forexample, a retrieval tool may include a personal computer, such as alaptop PC, which may be connected to transmit the data via any number ofwell-known means, including wireless transmission, telephone line, cableor DSL modem, ISDN lines, or other data transmission routes. However, itis to be understood that in certain embodiments, the retrieval systemneed not be connected to such a personal computer. Further, in anexample embodiment, the central location may be an office of aninsurance company, an injury analysis firm, or the like. Such a centrallocation may include various computer systems, including personalcomputers, servers, data storage devices, and the like.

The system of the present invention may be used by a user who is nottechnically proficient in accident reconstruction techniques. That is,the methods and system disclosed herein may be implemented by anon-technical user to analyze vehicle accidents. In this way, entitiesusing the system and methods, such as insurance companies and the like,can task non-technical, and thus lower cost, workers with analyzingvehicle accidents. Such analysis may include comparing accident claims(both personal and property injury) to an actual accident to determinewhether the claims are consistent with likely damage and/or injury.

In example embodiments, the data may be transmitted from a dataretrieval tool to the central location via the Internet. In certainembodiments, it may be desirable to transmit the data in a secure mannerto preserve the sanctity and reliability of the data. Alternately, datacollected from a retrieval tool may be stored on any number ofwell-known storage media including floppy discs, ZIP discs, CD ROM's,CD-RWs, disc drives, tape means, and the like. In such manner, the datacan later be downloaded to a computer at another location.

With respect to a computer system at a central location, in oneembodiment, the data may be transmitted and stored in a server or a datastorage device associated with such a server. Alternately, the data maybe transmitted to a personal computer unassociated with such a network.After receipt of such data, the central location may use the data aloneor in connection with other computer-based vehicular analysis tools. Inan example embodiment, the data obtained from the EDR may be used inconnection with a computer-based system to aid in analysis of a vehicleaccident. The data may be used to determine change in velocity of one ormore vehicles involved in the accident, impact severity of the accident,the principal direction of force acting on the vehicles, and otherparameters associated with such an accident, including, for example,pre-impact vehicle speeds, and sequence of impacts in a multi-vehicleimpact.

Such a computer-based system is a complementary technology to EDRsystems since EDR technology cannot derive point and angle of impact(i.e., impact configuration), nor can it independently assess accidentimplications to the other vehicle and its occupants. For these reasons,“black box” data is an important supplemental source of information inthe investigation of a claim. Traditional sources of information used toevaluate claims such as collision repair estimates and vehiclephotographs will continue to be important to any analysis of whathappened in a collision.

In certain embodiments, the methods described herein may be used inconnection with the system and methods described in U.S. Pat. No.6,381,561, the disclosure of which is hereby incorporated by reference.Similarly, the methods described herein may be used in connection withthe system and methods described in U.S. application Ser. No.09/018,632, the disclosure of which is hereby incorporated by reference.Further, the system and methods described herein may be used inconnection with the vehicle accident analysis system entitled WREXPERT,commercially available from the assignee hereof, future versions of theWREXPERT system, and other such systems, commercially available orotherwise.

An example embodiment of a computer-based system in accordance with theabove-referenced disclosures, namely the WREXPERT system, containsdatabases with information on over 28,000 private passenger autos,pickups, vans and SUVs. These specifications include physicaldimensions, mass attributes, and crash test data/performance. With thisinformation and a vehicle's collision repair estimates and damagephotographs, the computer-based system can determine impact severity toboth vehicles involved in the collision, injury potential to eachvehicle's occupants as well as highlight accident causation issues(point of impact, angle of impact, etc.). This analysis processfacilitates a liability determination based on the objective datadocumented in the repair process and recorded by the vehicle during theaccident event. Consequently, time related to attempting to reconciledisparate statements among witnesses and accident participants can beminimized. The analysis process can also help facilitate a science-basedestimation of impact severity, occupant motion and subsequent injurypotential of a vehicle collision.

The system and methods disclosed herein may also be used to identifydata inconsistencies that may be captured by an EDR system, such asinaccurate pre-crash speeds related to skidding or other scenariosinvolving a loss of traction. Since such a computer-based system mayemploy numerous analysis methodologies and its algorithms must alwayssatisfy the laws of physics, data inconsistencies can be quicklyidentified. For example, comparison of traditional impact severitytechniques (as described in U.S. Pat. No. 6,381,561) and accidentdescriptions can illuminate inconsistencies between the damages to thevehicle and the resulting impact severity and the EDR data. In certainembodiments, the EDR data can be weighted more (or less) heavily thantraditional impact severity techniques to more accurately measure impactseverity.

In an example embodiment, a computer system, such as a PC, includes aprocessor coupled to system memory via a bus. The bus may, for example,include a processor bus, local bus, and an extended bus. A nonvolatilememory, which may, for example, be a hard disk, read only memory(“ROM”), floppy magnetic disk, magnetic tape, compact disk ROM, otherread/write memory, and/or optical memory, stores machine readableinformation for execution by the processor. Generally, the machinereadable information is transferred to system memory via the bus inpreparation for transfer to processor in a well-known manner. Thecomputer system may also include an I/O (“input/output”) controllerwhich provides an interface between the bus and I/O device(s). In awell-known manner, information received by an I/O controller from I/Odevice(s) is generally placed on a bus and in some cases stored innonvolatile memory and in some cases is utilized directly by theprocessor or an application executing on the processor from systemmemory. I/O device(s) may include, for example, a keyboard, a mouse, anda modem. A modem may transfer information via electronic data signalsbetween the I/O controller and an information source such as anothercomputer which is coupled to the modem via, for example, a conductivemedia or electromagnetic energy.

The computer system may also include a graphics controller which allowsit to display information, such as a windows based graphical userinterface, on display in a well-known manner. It will be understood bypersons of ordinary skill in the art that computer system may includeother well-known components. Further, similar components may be presentin devices other than a PC, such as a PDA, pen-based device, advancedcellular phones, and the like, all of which may be referred to as acomputer. Further, similar components may be present in server and datastorage devices, which may be interconnected as a computer system.

Is important to note that EDR information is harvestable even when theair bag does not deploy in a collision. For example, in GM systems whenthe airbag system is “awakened” during a collision but the systemdetermines that the collision is not severe enough to warrant air bagdeployment, the event is referred to as a “near deployment event.” Innear deployment events, the data recorded and saved is temporarilystored for 250 ignition cycle counts (or approximately forty five daysof normal use). Should a vehicle be involved in another near deploymentevent before a previous near deployment event is cleared, the mostsevere event is stored for most EDR's. Should the subsequent collisionbe severe enough to warrant the deployment of an airbag (a deploymentevent), the data is permanently stored. Alternately, the EDR can storeboth types of events simultaneously, with separate storage areas fornear deployment and deployment data. This technology may also record twoor more impacts in an accident sequence if at least one of the impactsis severe enough to deploy the airbag.

Typically, EDR information may be valuable to more than high severity,high monetary value cases. It is harvestable in the frequent, minor-typecollisions and is very helpful in resolving “low impact” type claims.Since this data is time-sensitive, early recognition of harvestingopportunities will be critical in the claims process. Also, should aquestion arise concerning whether the data harvested is applicable tothe accident in question because of a claim of a subsequent collision,the data storage strategies insure that the worst-case event is beingevaluated. In injury claims, this gives the claimant every benefit ofthe doubt. Further, when an airbag deploys in an accident, the modulestoring data must be replaced. These modules should be identified andtreated as relevant evidence in the investigation and evaluation of anaccident. Accordingly, process revisions as to the salvage and storageof these parts in the collision repair process are warranted.

Data retrieval from an EDR is a very straightforward process and may beaccomplished in 5 to 10 minutes if the vehicle is not structurallycompromised in a fashion that makes the harvesting portals inaccessible.As shown in FIG. 2, in an exemplary embodiment, a crash data retrieval(“CDR”) system includes an interface module 100 which may be connectedto a personal computer (“PC”) 150 via cable 160 through a serial port ofthe PC. As discussed above, other forms of computers may be used toobtain the EDR information instead of a laptop PC as shown in FIG. 2. Inan example embodiment, the interface module 100 may be a Vetronix CrashData Retrieval System on which WINDOWS-based Vetronix software isloaded. Once the CDR software is activated on the PC, the CDR system maybe connected to one of two available ports on the vehicle via cable 120extending from the interface module 100. One port is the diagnostic linkconnector or OBDII port. In most vehicles, this port is found under thedashboard. This port is universal across all manufacturers in the U.S.and data may be harvested from this port using a standard cable. Shouldthis port be compromised, the data may be harvested directly from themodule storing the data (the SDM in GM vehicles) using a cablecompatible with the storage module. It is to be understood that variouscables may be used to harvest information from different makes andmodels. Further, in certain embodiments, such cabling may beunnecessary, as data may be retrieved using wireless technology incertain embodiments.

Once the cable connections are made, a harvesting session may begin. Inan example embodiment, the harvester of the information enters theirname, the case or claim number, the date the information was harvested,the date of the accident and the VIN. Once this data is entered, theharvesting process may be activated from the PC and the data isdownloaded to the PC and its designated storage medium. For ease ofidentification, the filename of the downloaded information may be theVIN.

It is to be understood that to harvest information from a EDR system,the vehicle need not have power. In these cases, the CDR system can bepowered from another vehicle using a cigarette lighter adapter or fromtraditional power outlets in a repair shop environment. Further, theharvesting of “black box” data by a CDR system does not erase theinformation in the data storage module. In other words, the same “blackbox” information related to an accident event can be harvested multipletimes by parties with different interests. In this regard, the datacollected is objective and favors no party or, in other words, “theknife cuts both ways”. Since the data is objective and has welldocumented rates of accuracy, it can eliminate costly debates overvarious aspects of an accident event.

Potential harvesters of “black box’ data include, for example,researchers, forensic experts and investigators, law enforcementpersonnel, attorneys, government agencies, insurance company personnel,independent adjusters/appraisers, auto repair facility personnel andairbag service and installation companies.

For the insurance industry, a tremendous opportunity exists to leverageexisting inspections of vehicles during the collision repair process andharvest EDR information during the same time the vehicle is photographedand a repair estimate is written. If accomplished during theseprocesses, the information becomes available early in the evaluation ofa claim.

Once “black box” data is harvested, the data may then be analyzed. Inthose circumstances where the harvester of the information will also beanalyzing the data, no data transmission is required. In thesesituations, chain of custody issues related to the data, if any, areminimal. Only data and harvesting integrity can be questioned. Becauseexpert harvesting and analysis is cost prohibitive on a large-scalebasis, the present invention provides an alternative.

In an example embodiment, a secure web site may be provided to whichharvested “black box” data can be uploaded and maintained withoutexposure to corruption or subsequent alteration. The unique features ofthis web site include: 1) transmission of original hexadecimal dataretrieved from the EDR system to prevent the possibility of datatampering; 2) administrative data capture to document chain of custody;and 3) accessibility of the data in an unalterable form by onlyauthorized personnel. Further, the data may be transferred in encryptedform as a further security measure. In an example embodiment, thewebsite may be resident on a server or other computer. Using thepreviously described computer-based system and aforementioned web site,“black box” data can be harvested in Florida, uploaded and subsequentlyanalyzed in California in the same business day for example.

In conjunction with other technologies, the following factors related toinjury causation can be assessed with EDR information: impact severity;restraint system utilization; injury mechanisms; and injury severity.

Additionally, the following factors relevant to an accident causationand liability determination analysis can be assessed with the use ofother technologies: point of impact; angle of impact; pre-impact speeds;pre-impact braking times and distances; human factors, restraint systemutilization, and determining impact sequence.

Some EDR's record the severity of a crash event by calculating thelongitudinal Delta-V. The Delta-V is calculated by integrating theaccelerometer data. Internally, the SDM calculates the Delta-V every1.25 milliseconds, but records the data at 10 milliseconds intervals.

Some EDR's record pre-crash data in addition to the crash severity. Thepre-crash data consists of the vehicle's speed, engine speed, percentthrottle, and brake switch circuit status. Each of these parameters issampled at 1-second intervals and stored in a buffer containing 5samples. Upon algorithm enable (AE), this buffer is recorded in the EDR.Algorithm enable is the time at which the EDR “wakes up” and decidesthere is an event worthy of its attention.

There are 3 different types of EDR's used in late model GM vehicles(1996 and newer) which record pre-impact speed, longitudinal Delta-V, orboth. Although the example used is with reference to GM models, it is tobe understood that these EDR's may be placed on vehicles of any make ormodel. These types are summarized in Table 1. A simple impact model maybe used in conjunction with data from each type of EDR to assess thecollision severity for both vehicles.

TABLE 1 Type Longitudinal Delta-V Pre-impact Speed 1 ✓ ✓ 2 ✓ 3 ✓

An algorithm in accordance with the present invention uses a simpleplanar impact model based on conservation of momentum principles. In anexample embodiment, the model may be subject to the followingsimplifications. First, vehicles are constrained to motion in the plane,i.e. longitudinal translation, lateral translation, and rotation.Second, only collision forces are considered significant. Tire frictionforces are neglected. This has the effect of overestimating impactseverity in most cases. Third, instantaneous transfer of momentum thatis the impulse acting on each vehicle has an infinitesimally smallduration. This impulse represents the resultant collision impulse andacts at a single point on each vehicle. This point is referred to as thecenter of impulse. Fourth, relative motion between the vehicles' impulsecenters is constrained by coefficients of restitution and slip. Fifth,colliding bodies are modeled as rigid, with constant mass and inertialproperties.

Table 2 summarizes the nomenclature used in the derivation of the impactmodel. FIGS. 3 and 4 illustrate the vehicle geometry and coordinatesystems used throughout the derivation.

TABLE 2 Meaning Symbol V Velocity vector ω Angular velocity vector θHeading angle R Position vector Φ Angle to impulse center relative toheading P Impulse vector M Mass I_(Z) Moment of inertia ε Coefficient ofrestitution Γ Angle to collision plane with respect to fixed Earthreference σ Coefficient of slip Subscripts/ Superscripts 1,2 Vehiclenumber C Center of impulse n,t Normal/tangential coordinates x,y FixedEarth coordinates R Relative (e.g. relative velocity) • (dot) Rate (e.g.yaw rate) ′ (prime) Post impact (e.g. post-impact velocity)The velocity of the impulse center, C, for each vehicle is given by{right arrow over (V)} _(c1) =+{right arrow over (V)} ₁+{right arrowover (ω)}₁ ×{right arrow over (r)} ₁  (1)V _(c1n) =V _(1n)−{dot over (θ)}₁ r _(1t)  (1a)V _(c1t) =V _(1t)+{dot over (θ)}₁ r _(1n)  (1b){right arrow over (V)} _(c2) =+{right arrow over (V)} ₂+{right arrowover (ω)}₂ ×{right arrow over (r)} ₂  (2)V _(c2n) =V _(2n)−{dot over (θ)}₂ r _(2t)  (2a)V _(c2t) =V _(2t)+{dot over (θ)}₂ r _(2n)  (2b)The relative velocity of the impulse centers prior to the collision isgiven by{right arrow over (V)} _(rc) ={right arrow over (V)} _(c1) −{right arrowover (V)} _(c2) ={right arrow over (V)} ₁+{right arrow over (ω)}₁×{right arrow over (r)} ₁−({right arrow over (V)} ₂+{right arrow over(ω)}₂ ×{right arrow over (r)} ₂)  (3)During the collision, the relative velocity of the impulse centers ischanged byΔ{right arrow over (V)} _(rc)=({right arrow over (V)} _(rc) ′−{rightarrow over (V)} _(rc))=({right arrow over (V)} _(c1) ′−{right arrow over(V)} _(c2)′)−({right arrow over (V)} _(c1) −{right arrow over (V)}_(c2))=({right arrow over (V)} _(c1) ′−{right arrow over (V)}_(c1))−({right arrow over (V)} _(c2) ′−{right arrow over (V)}_(c2))  (4)Δ{right arrow over (V)} _(rc) =[{right arrow over (V)} ₁′+{right arrowover (ω)}₁ ′×{right arrow over (r)} ₁−({right arrow over (V)} ₁+{rightarrow over (ω)}₁ ×{right arrow over (r)} ₁)]−[{right arrow over (V)}₂′+{right arrow over (ω)}₂ ′×{right arrow over (r)} ₂−({right arrow over(V)} ₂+{right arrow over (ω)}₂ ×{right arrow over (r)} ₂)]  (4a)The relative velocity of the impulse centers after the collision isgiven by{right arrow over (V)} _(rc) ′=+{right arrow over (V)} _(rc) +Δ{rightarrow over (V)} _(rc)  (5)V _(rc) _(n) ′=V _(rc) _(n) =[(V _(1n) −′V _(1n))−({dot over (θ)}₁′−{dotover (θ)}₁)r _(1t)]−[(V _(2n) −′V _(2n))−({dot over (θ)}₂′−{dot over(θ)}₂)r _(2t)]  (5a)V _(rc) _(t) ′=V _(rc) _(t) =[(V _(1t) −′V _(1t))+({dot over (θ)}₁′−{dotover (θ)}₁)r _(1n)]−[(V _(2t) −′V _(2t))+({dot over (θ)}₂′−{dot over(θ)}₂)r _(2n)]  (5b)The impulse-momentum equations for the two vehicles{right arrow over (P)}=m ₁({right arrow over (V)} ₁ ′−{right arrow over(V)} ₁)−{right arrow over (P)}=m ₂({right arrow over (V)} ₂ ′−{rightarrow over (V)} ₂)  (6)P _(n) =m ₁(V _(1n) ′−V _(1n))−P _(n) =m ₂(V _(2n) ′−V _(2n))  (6a)P ₁ =m ₁(V _(1t) ′−V _(1t))−P ₁ =m ₂(V _(2t) ′−V _(2t))  (6b){right arrow over (r)} ₁ ×{right arrow over (P)}=I _(z1)({right arrowover (ω)}₁′−{right arrow over (ω)}₁){right arrow over (r)} ₂ ×−{rightarrow over (P)}=I _(z2)({right arrow over (ω)}₂′−{right arrow over(ω)}₂)  (7)r _(1n) ×P _(t) −r _(1t) P _(n) =I _(z1)({right arrow over (ω)}₁′−{rightarrow over (ω)}₁)−r _(2n) P _(t) +r _(2t) P _(n) =I _(z1)({right arrowover (ω)}₂′−{right arrow over (ω)}₂)  (7a)Combining equations 5, 6, and 7 gives

$\begin{matrix}{V_{{rc}_{n}}^{\prime} = {V_{{rc}_{n}} + \frac{P_{n}}{m_{1}} - \left( {\frac{r_{1t}r_{1n}P_{t}}{I_{z\; 1}} - \frac{r_{1\; t}^{2}p_{n}}{I_{z\; 1}}} \right) - \left\lbrack {\frac{- P_{n}}{m_{2}} - \left( {\frac{{- r_{2\; t}}r_{2\; n}P_{t}}{I_{z\; 2}} + \frac{r_{2\; t}^{2}P_{n}}{I_{z\; 2}}} \right)} \right\rbrack}} & (8) \\{V_{{rc}_{n}}^{\prime} = {V_{{rc}_{n}} + {P_{n}\left( {\frac{1}{m_{1}} + \frac{1}{m_{2}} + \frac{r_{1t}^{2}}{I_{z\; 1}} + \frac{r_{2t}^{2}}{I_{z\; 2}}} \right)} - {P_{t}\left( {\frac{r_{1t}r_{1n}}{I_{z\; 1}} + \frac{r_{2t}r_{2n}}{I_{z\; 2}}} \right)}}} & \left( {8a} \right) \\{V_{{rc}_{n}}^{\prime} = {V_{{rc}_{t}} + \frac{P_{t}}{m_{1}} + \frac{r_{1n}^{2}P_{t}}{I_{z\; 1}} - \frac{r_{1n}r_{1t}P_{n}}{I_{z\; 1}} - \left\lbrack {\frac{- P_{t}}{m_{2}} - \frac{r_{2n}^{2}P_{t}}{I_{z\; 2}} + \frac{r_{2n}r_{2t}P_{n}}{I_{z\; 2}}} \right\rbrack}} & (9) \\{V_{{rc}_{n}}^{\prime} = {V_{{rc}_{t}} - {P_{n}\left( {\frac{r_{1n}r_{1t}}{I_{z\; 1}} + \frac{r_{2n}r_{2t}}{I_{z\; 2}}} \right)} + {P_{t}\left( {\frac{1}{m_{1}} + \frac{1}{m_{2}} + \frac{r_{1n}^{2}}{I_{z\; 1}} + \frac{r_{2n}^{2}}{I_{z\; 2}}} \right)}}} & \left( {9a} \right)\end{matrix}$Equations 8 and 9 can be rewritten asV _(rc) _(n) ′=V _(rc) _(n) +c ₂ P _(n) −c ₃ P _(t)  (10)V _(rc) _(t) ′=V _(rc) _(t) +c ₃ P _(n) −c ₁ P _(t)  (11)where,

$\begin{matrix}{c_{1} = {\frac{1}{m_{1}} + \frac{1}{m_{2}} + \frac{r_{1n}^{2}}{I_{z\; 1}} + \frac{r_{2n}^{2}}{I_{z\; 2}}}} & (12) \\{c_{2} = {\frac{1}{m_{1}} + \frac{1}{m_{2}} + \frac{r_{1t}^{2}}{I_{z\; 1}} + \frac{r_{2t}^{2}}{I_{z\; 2}}}} & (13) \\{c_{3} = {\frac{r_{1t}r_{1n}}{I_{z\; 1}} + \frac{r_{2t}r_{2n}}{I_{z\; 2}}}} & (14)\end{matrix}$

Coefficients of restitution and slip are introduced to relate thepost-impact relative velocity of the impulse centers to their pre-impactrelative velocity.

The coefficient of restitution is the ratio of the post-impact relativevelocity to the pre-impact relative velocity in the direction normal tothe collision plane, i.e.,

$\begin{matrix}{ɛ = {{\frac{- V_{{rc}_{n}}^{\prime}}{V_{{rc}_{n}}}ɛ} \subseteq \left\lbrack {0,1} \right\rbrack}} & (15)\end{matrix}$

The slip coefficient is the ratio of the post-impact to pre-impactrelative velocity along the tangential direction, i.e.,

$\begin{matrix}{\sigma = {{\frac{V_{{rc}_{t}}^{\prime}}{V_{{rc}_{t}}}\sigma} \subseteq \left\lbrack {{- 1},0} \right\rbrack}} & (16)\end{matrix}$The equations 10, 11, 15, and 16 can be solved using Cramer's rule,i.e.,

$\begin{matrix}{P_{n} = \frac{{{V_{{rc}_{n}}\left( {1 + ɛ} \right)}c_{1}} + {{V_{{rc}_{t}}\left( {1 - \sigma} \right)}c_{3}}}{c_{3}^{2} - {c_{1}c_{2}}}} & (17) \\{P_{t} = \frac{{{V_{{rc}_{n}}\left( {1 - \sigma} \right)}c_{3}} + {{V_{{rc}_{t}}\left( {1 + ɛ} \right)}c_{2}}}{c_{3}^{2} - {c_{1}c_{2}}}} & (18)\end{matrix}$

The impulses (given by 17 and 18) can be substituted in theimpulse-momentum equations directly to solve for the post-impactvelocities.

These equations (17 and 18) show that the impulse vector (and hence ΔV)is a function of the closing velocity, vehicle mass properties, vehicleconfiguration (r_(n), r_(t) in constants c1, c2, c3), andrestitution/slip coefficients (ε,σ). When the restitution/slipcoefficients are both equal to zero, there is no relative velocitybetween the collision centers post-impact. In other words, the vehicles'collision centers act like a pin joint, connecting two bodies in planarmotion, which can still have relative rotational velocity. This type ofcollision is referred to as a fully plastic collision. A fully elasticcollision occurs when the restitution/slip coefficients take on theirmaximum absolute values, 1 and −1, respectively. In a fully elasticcollision, the component of the relative velocity normal to thecollision plane changes sign from pre-impact to post-impact. Therelative velocity tangential component remains constant. The fullyplastic collision and fully elastic collision are depicted in FIG. 5.Differences in vehicle position and heading from pre-impact topost-impact are shown for illustrative purposes only. This model assumesan instantaneous transfer of momentum in which the vehicles' positionand heading remain fixed.

The coefficients of restitution and slip are not required to have thesame absolute values. However, when they do, i.e., when σ=−ε, equations17 and 18 can be written as

$\begin{matrix}{P_{n} = {\left\lbrack \frac{{V_{{rc}_{n}}c_{1}} + {V_{{rc}_{t}}c_{3}}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (19) \\{P_{t} = {\left\lbrack \frac{{V_{{rc}_{n}}c_{3}} + {V_{{rc}_{t}}c_{2}}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (20)\end{matrix}$

Using equations 19 and 20 simplify the model because they areindependent of the collision plane angle, Γ. Notice that equations 19and 20 are just the equations for the fully plastic impact multiplied bythe scalar (1+ε). Since these equations are not dependent on the angleΓ, this angle can be chosen for convenience. For example, FIG. 6 shows atwo-vehicle collision configuration in which the fixed-Earth referenceframe and collision reference frame are chosen such that they have thesame orientation as the reference frame of vehicle #1. This conventionwill be used for the impact algorithm employing “black box” data.

The next sections describe the various EDR's and how the informationextracted therefrom may be used in conjunction with the model to assessimpact severity, and, in some cases, to predict the pre-impact velocityof the “struck” vehicle.

A type 1 EDR records the vehicle's pre-impact speed and longitudinal ΔV.It is assumed that the vehicle with the EDR (striking vehicle) is headedin the same direction as the x-axis of a fixed earth non-inertialreference frame (see FIG. 6). It is further assumed that this directionis parallel with the n-axis of the n-t reference frame. Two additionalassumptions are required for the solution: (1) neither vehicle has anypre-impact rotational velocity, ω₁=ω₂=0; and (2) neither vehicle has anypre-impact sideslip velocity, V_(y1)=V_(y2)=0.

Based on these assumptions equations 19 and 20 become

$\begin{matrix}{P_{n} = {\left\lbrack \frac{{\left( {V_{1x} - {V_{2x}\cos\;\theta_{2}}} \right)c_{1}} - {V_{2x}\sin\;\theta_{2}c_{3}}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (21) \\{P_{t} = {\left\lbrack \frac{{\left( {V_{1x} - {V_{2x}\cos\;\theta_{2}}} \right)c_{3}} - {V_{2x}\sin\;\theta_{2}c_{2}}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (22)\end{matrix}$

The terms cos θ₂ and sin θ₂ are from the conversion from thefixed-vehicle reference frame of vehicle #2 to the n-t reference frameFrom equation 6a, equation 21 can be rewritten as

$\begin{matrix}{{m_{1}\Delta\; V_{1x}} = {\left\lbrack \frac{{\left( {V_{1x} - {V_{2x}\cos\;\theta_{2}}} \right)c_{1}} - {V_{2x}\sin\;\theta_{2}c_{3}}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (23)\end{matrix}$which can be solved directly for V_(2x), i.e.,

$\begin{matrix}{V_{2x} = \frac{\left\lbrack {{V_{1x}c_{1}} - \frac{m_{1}\Delta\;{V_{1x}\left( {c_{3}^{2} - {c_{1}c_{2}}} \right)}}{\left( {1 + ɛ} \right)}} \right\rbrack}{{\cos\;\theta_{2}c_{1}} - {\sin\;\theta_{2}c_{3}}}} & (24)\end{matrix}$

This value can then be substituted into equation 22 to solve for thetangential component of the resultant impulse vector (and ΔV_(2y)).

Further, the principal direction of force (PDOF) can be determined. Thedetermination of PDOF may be done in connection with informationobtained from the user. Such user input information may include damageinformation, which in an example embodiment may include damage patterninformation regarding which components needed repair or replace, crushdepth of certain components, points of contact, or the like. Further,user input information may include a description of the accident. Suchdescription may include pre-accident vehicle motion, direction of travelfor the vehicles, turning patterns, and the like. Based on thisinformation, the point of impact on the vehicles and the PDOF may bedetermined.

After determination of the points of impact and PDOF of the vehicles,impact severity may be determined. In an accident in which one vehiclehas an EDR, the impact severity of the other vehicle may be determinedusing the EDR data of the first vehicle and the points of impact andPDOF previously determined for the vehicles. Specifically, the Delta-Vof the first vehicle may be obtained from the EDR and used in the aboveequation 24 to determine pre-impact speed of the second vehicle, whichmay then be used in equation 22 above to determine the impact severityof the second vehicle.

In certain embodiments, the Delta-V obtained from the EDR may beadjusted to correct for any underestimation inherent in the EDR, asdiscussed below. Further, to more accurately determine impact severity,the adjusted Delta-V (which in certain embodiments may include a highand a low range), may be input into a Monte Carlo simulation module,also discussed more fully below.

Based on the information determined by the system based on the EDR data(and in combination with other data), impact severity can thus bedetermined. In addition to Delta-V, the principal direction of forceupon the vehicles can be determined based on the black box data and thevehicle damage. From this information, injury potential can also beevaluated. That is, given a particular value for Delta-V and principaldirection of force, a range of potential injury can be determined. Sucha determination may be made by use of rule-based logic, in which thevalues of Delta-V and PDOF can be compared, along with information as towhether the occupant was properly restrained. Based on the results ofsuch injury potential, an insurance company or other interested partycan then analyze whether a claimant's injury claim is consistent withthat determined by the system. Further, an analysis of the point andangle of impact can be used to resolve issues relating to which vehiclehad the right of way, which may be relevant to fault analysis and/orcontributory negligence. Similarly, an analysis of the pre-impact speedof the vehicle having the EDR (and/or the other vehicle) to the speedlimit for the location of the accident may indicate that one or both ofthe vehicles were exceeding the speed limit.

If a Type 1 black box is used, i.e., it has both pre-impact speed andDelta-V information, the pre-impact speed calculated for the “struck”vehicle should agree with the user's description of the accidentscenario. This description may be input into the system by a user frominformation derived from an accident report or the like. For example,the user may indicate that the “struck” vehicle was stopped prior toimpact. In contrast, the black box information and the laws of physicsmay suggest that the vehicle was moving prior to impact. Another exampleis that the user indicates that the “struck” vehicle is moving forwardprior to impact and the black box analysis indicates the “struck”vehicle was backing up prior to impact. In such instances, discrepanciesmay be resolved in part by resort to the methods shown in FIGS. 9A and Band 14-17, discussed below. Alternately, a user may be provided with aprompt, such as a graphical user interface, in order to choose to ignoreEDR data in the analysis of a given accident.

A type 2 EDR records pre-impact speed only. To assess impact severityusing information from this type of EDR, the struck vehicle must be atrest prior to impact (or its velocity must be known or reasonablybounded). If the struck vehicle is at rest, then the pre-impact speedmeasured by the EDR is a good measure of the closing speed. Equations 19and 20 can be used to solve for the ΔV of both vehicles.

A type 3 EDR records longitudinal ΔV only. To assess impact severityusing information from this type of EDR, the struck vehicle must be atrest prior to impact (or its velocity must be known or reasonablybounded). Using the measured ΔV from the EDR records, the normalcomponent of the impulse vector can be calculated using equation 6a.Assuming the striking vehicle has no pre-impact sideslip velocity,equation 19 can be used to solve for the normal component of therelative velocity, i.e.,

$\begin{matrix}{V_{{rc}_{n}} = \frac{P_{n}\left( {c_{3}^{2} - {c_{1}c_{2}}} \right)}{c_{1}\left( {1 + ɛ} \right)}} & (25)\end{matrix}$

This value can then be substituted into equation 20 to solve for thetangential component of the crash impulse, i.e.

$\begin{matrix}{P_{t} = {\left\lbrack \frac{V_{{rc}_{n}}c_{3}}{c_{3}^{2} - {c_{1}c_{2}}} \right\rbrack\left( {1 + ɛ} \right)}} & (26)\end{matrix}$

The coefficient of restitution may be based on the following model,which was fit from experimental dataε=0.1+0.9e ^(−(0.34+0.16V) ^(c) ⁾  (27)where, Vc is the closing velocity in mph. The data and best-fit line areshown in FIG. 8A.

In certain embodiments, a Monte Carlo simulation may be used to accountfor uncertainty in the input variables. Each of the crash parametersmeasured by the EDR is subject to measurement errors. These errors arebased on the accuracy of the instrument, the measurement technique, orboth. Table 3 shows the resolution and accuracy of the parametersmeasured by an example EDR.

TABLE 3 Para- Resolu- How meter Full Scale tion Accuracy Measured WhenUpdated ΔV +−55.9 0.4 mph ~±10% Integrated Recorded every mphacceleration 10 msec, calculated every 1.25 msec. Vehicle 158.4 mph 0.6mph ±4% Magnetic Vehicle speed speed pickup changes by >0.1 mph Engine16383 ¼ ±1 RPM Magnetic RPM changes speed RPM RPM pickup by >32 RPM.Throttle 100% 0.4% ±5% Rotary Throttle posi- Position Wide openpotentio- tion changes by throttle meter >5%.

In the Monte Carlo simulation, each input variable is randomly sampledfrom a uniform distribution. The bounds of these distributions are basedon reasonable limits. In the case of EDR data, the bounds may be basedon the accuracy of the parameter listed in Table 3. For example, if a ΔVof 10 mph is measured, then a uniform distribution of 9 to 11 mph willbe used for the simulation. The process of randomly sampling the inputdistributions and computing ΔV is repeated in a loop until the desiredsample size is achieved, or until the maximum number of loops has beenexceeded.

FIG. 7 is a flow chart of an exemplary Monte Carlo simulation module inaccordance with the present invention. As shown in FIG. 7, module 200includes step 210 in which the Monte Carlo simulation is begun. At step220, the number of loops of the process is checked against apredetermined maximum number of loops to determine whether the desiredsample size has been achieved or whether the maximum number of loops hasbeen exceeded. In an example embodiment, the desired sample size mayextend to approximately 10,000 iterations.

If it is determined that the desired sample size has not been achievednor the maximum number of loops exceeded, control passes to step 230 inwhich values are randomly sampled from a uniform distribution for theinput variable 240. As discussed above, the uniform distribution may bebased on reasonable limits dependent upon the accuracy of a givenvariable. After randomly sampling the value at step 230, control passesto step 250 which determines whether the results are consistent withother evidence. For example, if it is known that the struck vehicle wasmoving forward at the moment of impact, then all calculations thatresult in a negative pre-impact speed should be rejected. If it isdetermined at step 250 that the results are not consistent, control thenpasses to step 220 in which the number of loops is incremented by oneand step 220 is performed again. If it is determined at step 250 thatthe results are consistent, control passes to step 260 in which thecomputed value is added to the distribution for the input variable.Then, control passes to step 220. When the maximum number of loops hasbeen exceeded or the sample size has been achieved, control passes fromstep 220 to step 270 in which the Monte Carlo simulation is concluded.The Monte Carlo results thus provide a range of feasible impact severityanswers which satisfy physical laws based on the initial inputs into thesystem.

Depending on the EDR used, it may be desirable in certain embodiments tomake an adjustment to the reported change in velocity value prior tofurther processing. For example, the EDR found in GM vehicles tends tounderestimate the change in velocity (ΔV), as the EDR does not startcalculating ΔV (via integration of an accelerometer signal) until itsenses an event that warrants attention. Since the EDR does not enableits algorithm until a threshold event is sensed, it excludes a smallpart of the acceleration pulse from the integration. Tests have shownthat the underestimate is a function of both the pulse shape andduration. To account for this underestimate, the model shown in FIG. 8Bwas developed.

As shown in FIG. 8B, the raw data points represent the ΔV measured bythe EDR in vehicle-vehicle crash tests and the corresponding ΔV error(underestimate). To account for this underestimate, the range of ΔV usedin the Monte Carlo simulation as discussed above and shown in FIG. 7 maybe adjusted accordingly.

The uniform ΔV distribution used in the Monte Carlo analysis may thus bedefined by lower and upper limits, ΔV_(low) and ΔV_(high), respectively.These values may be defined by the value of ΔV recorded by the EDR, theaccuracy of the sensor, and an adjustment made according to the model ofFIG. 8B.

In other words, ΔV_(low) is given by:ΔV _(low) =ΔV _(SDM)−0.10(ΔV _(SDM))+V _(err) _(—) _(lower95)  (28)and Delta-V_(high) is given by:ΔV _(high) =ΔV _(SDM)−0.10(ΔV _(SDM))+V _(err) _(—) _(upper95).  (29)Thus, adjusted values for Delta-V may be used in certain embodiments tomore accurately reflect the actual change in velocity.

FIGS. 9A and B together are a flow chart of one embodiment of an examplemodule for processing black box data in accordance with the presentinvention. More specifically, the module of FIGS. 9A and B may be usedto determine whether black box information exists for more than oneevent and process the information accordingly. As shown in FIG. 9A,module 300 operates as follows. At step 310, vehicle black boxinformation is obtained by a computer system which operates software toperform the steps of the method. At step 315, the black box informationis checked to determine whether deployment data is available. If suchdata are available, control passes to step 320, which determines whethernear deployment data is available. If such data are present, controlpasses to step 330, which determines whether the near deployment dataand the deployment data are from the same event. Such a determinationmay be made, for example, by an analysis of ignition cycle countsreported by the EDR in connection with the deployment and neardeployment data. If the ignition cycles match, the data are from thesame event If it is determined that the near deployment data anddeployment data are from the same event, control passes to step 340,which indicates that there are multiple events to analyze from the blackbox data. Further, at step 340, the higher severity event is chosen foruse in the analysis.

From step 340, control passes to step 345, in which user inputinformation, such as accident description information as input by thesystem user, is received. In an example embodiment, the accidentdescription information may include a narrative description of theaccident, including such information as impact sequence, direction ofimpact, weather conditions, claimed passenger injuries, vehicleconfiguration, pre-accident motion, and the like.

At step 315, if it is determined that no deployment data is available,control passes to step 325, in which it is determined whether neardeployment data is available. If it is, control passes to step 335,which indicates that there is a single event from the black box data toanalyze. Step 335 is also entered from step 330 if it is determined thatthe near deployment data and the deployment data are not from the sameevent. From step 335, control passes to step 345, as discussed above.

If, at step 325, it is determined that there is no near deployment dataavailable, control passes to step 350, at which it is determined whetherthe event is below the black box data threshold. In an exampleembodiment, such a threshold may the point at which the EDR isactivated, which may be for impacts occurring at approximately 4 mph. Ifthe event is below the threshold, black box data is not used in ananalysis of the vehicle accident, as reflected at step 355. If the eventis above the threshold, control passes to step 335 and 345, as discussedabove.

Next, at step 360, the black box algorithm is performed. The particularalgorithm varies depending on the type of EDR present, as will bediscussed more fully with respect to the general flow charts of FIGS. 12and 14 and the more specific flow charts of FIGS. 15-17. Next, controlpasses in one of two steps, depending on whether there was a singleevent or multiple events. If there was a single event, control passes tostep 365 in which checks are made to determine whether anyinconsistencies exist. Then, control passes to step 370, in which areport of impact severity and direction of impact are made for thevehicles involved in the collision. In an example embodiment, thecalculations for those values are made in accordance with the abovediscussion and equations.

If multiple events occurred, control passes from step 360 to step 380,in which the sequence of events is determined. For example, a three carcollision may occur in which a first vehicle collides with a secondvehicle, which then strikes a third vehicle. In such a collision, thesequence may be determined by reviewing the EDR data along with externalinformation, such as an accident report. Using this information, thesequence may be determined.

Then control passes to step 385, in which a check is made forinconsistencies in the data input. Then control passes to step 390,where a report of impact severity and information on the timing ofevents and inconsistencies in the data is made for the vehicles involvedin the collision.

FIG. 10 is a flow chart of an example seatbelt module 400 forverification of information provided by the EDR. As shown in FIG. 10,module 400 begins with step 410, in which it is determined whether theseatbelt was on at the time of the accident. This is determined byanalysis of the driver's belt switch circuit status, as shown in FIG. 1.At step 420, this indication is compared with user input, which is basedupon information received by the user from accident reports or the like.At step 430, it is determined whether there are discrepancies betweenthe black box data and the user input. If there are, control passes tostep 440, in which the user is requested to input information to resolvethe discrepancy. In one embodiment, the user may be provided with agraphical user interface to select whether to use the EDR data or theuser-provided data.

Whether there is no inconsistency at step 430 or the inconsistency isresolved at step 440, control passes to step 450, in which rules-basedlogic is applied to the information as to seat belt usage. In an exampleembodiment, this rules-based logic includes occupant kinematics andinterior interaction logic. For example, the interior environment (basedon data in the system as to the particular vehicle) may be analyzed incombination with restraint usage (both passive and active). Further,direction and severity of impact may be analyzed to determine thepossible kinematics of the occupants. For example, based on the severityof the impact, direction of the impact, occupant positioning and the useof restraint system(s), the system may determine that the occupantmovement of the driver could have caused him to bump his head againstthe steering wheel (or not).

From this analysis, a report is generated at step 460, which reports onthe probability of interior interactions, occupant movement, andrestraint system(s) usage, and direction of impact. Such information isuseful, for example, in determining comparative negligence, if occupantswere not wearing seat belts. Further, such information is useful indetermining whether claimed injuries are consistent with the likelykinematics of the injured person. That is, the user or the system cancompare the determined likely kinematics against a claimed injury (orclaimed kinematics from an accident report) to guard against possiblefraud.

FIG. 11 is a flow chart of an example module 500 for analysis of humanfactors in accordance with the present invention. Such an analysis canaid in determination of whether human factors, such as inaction ordelayed action, played a part in causing an accident. As shown in FIG.11, module 500 begins at step 510, in which the system analyzes when thebrakes were applied. This information may be obtained from, for example,the pre-crash data obtained from the EDR, which indicates the time(before algorithm enable) at which the brake switch circuit (and thusthe brakes) was activated. At step 520, the speed when the brakes wereapplied is also analyzed by the system. This information may also beobtained from the EDR, which indicates the vehicle speed at the timewhen the brake switch circuit was activated.

The information obtained at steps 510 and 520 may then be used at step530 in a calculation of the distance from when the brakes were applieduntil impact. Next, at step 540, the system accepts an input ofinformation regarding the scene of the accident. Such information mayinclude, for example, weather conditions, skid marks, type of roadway,vehicle location, and the like. At step 550, human factors logic isapplied to the information in order to generate a report on humanfactors at step 560. In an example embodiment, the human factors logicmay determine whether the driver of the vehicle containing the EDRrecognized that an accident was imminent and/or could have avoided theaccident by earlier application of the brakes or the like.

Since data from the EDR is a direct measurement of the crash, the impactseverity calculated from this data may be compared to, combined with,and/or possibly be given preference over other methods currently used bya computer-based system. For example, the impact severity calculatedfrom the EDR data may be compared to impact severity determined by acomputer implemented method for accident analysis, such as thatdisclosed in U.S. Pat. No. 6,381,561.

FIG. 12 is a flow diagram of an example method according to the presentinvention in which a Delta-V determined from EDR data is combined withsuch information determined without EDR data. As shown in FIG. 12,method 600 begins at step 610, in which the contents of an EDR data fileobtained from the vehicle is validated. Such validation includes, forexample, a check of the VIN number to confirm that the file is for thecorrect vehicle to be analyzed. When the file contents have beenvalidated, control passes to step 620 in which an algorithm is selectedbased on the type of event data recorder, for example, that used in GMor Ford models. Such a selection may be based in an example embodimenton analysis of the VIN number, as the manufacturer is coded therein.

Depending on the particular recorder used, the system will choose apreselected algorithm for the type of EDR, as will be discussed in moredetail in connection with FIGS. 14-17. Next, at step 630, a Monte Carlosimulation is run to generate distributions for Delta-V. In an exampleembodiment, the simulation may be run in accordance with the flowdiagram of FIG. 7. Block 635 shows an example list of simulationvariables, which may include pre-impact speed, Delta-V, mass and Izz.Furthermore, for certain EDRs, for example, those used on GM models, thesystem may use the algorithms set forth above as equations 28 and 29 toaccount for the underestimation of change in velocity

After the Monte Carlo analysis has been completed, control passes tostep 640, in which the resultant Delta-V is combined with the results ofDelta-V for the same vehicle accident calculated via one or more othermethods. Such methods may include those disclosed in U.S. Pat. No.6,381,561. Because of the reliability of the Delta-V obtained from EDRdata, this value may be weighted more heavily. In the example embodimentof FIG. 12, the Delta-V obtained via the EDR data may be weighted 10times that of the other data points. However, in other embodiments, theEDR-obtained Delta-V may be weighted more or less than that determinedby other means. In such manner, an accurate determination of Delta-V maybe made.

In certain embodiments, it may be desirable to determine theconfiguration of the accident to aid in determining impact severity. Inone such embodiment, an accident configuration module calculates thepoint of impact and angle of impact between two vehicles in a collision.It does this by first gathering information about the property damage tothe subject vehicles, i.e., where on the vehicles is there contact/crushdamage and what is the extent of this contact/crush damage (i.e., howwide/how deep). The module then computes the center of the damaged areaon each vehicle. The vehicles are positioned such that the damagecenters (or centroids) on each vehicle coincide. Finally, anoptimization problem is solved to compute the “best” angle of impact.This optimal impact angle simultaneously maximizes the overlappingdamaged area, while minimizing any overlap of non-damaged vehicle areas(see FIG. 13). In certain embodiments, the user may have the option ofoverriding this optimal configuration with a user-defined accidentconfiguration.

FIG. 14 is a flow chart of another example embodiment of a moduleaccording to the present invention using black box data to analyze avehicle accident. This embodiment determines what type of EDR (Type 1,2, or 3) is accessed and a Monte Carlo simulation is run on the dataobtained, the results of which are then analyzed for consistency withphysical laws. As discussed above, different information is obtaineddepending on the type of EDR which is available. For example, a type 1EDR records data relating to Delta-V, pre-crash speed, pre-crash enginespeed, pre-crash percentage throttle and pre-crash braking. A type 2 EDRrecords data relating to pre-crash speed, pre-crash engine speed,pre-crash percentage throttle and pre-crash braking. Finally, a type 3EDR records Delta-V data only. While the following discussion relates tothese types of EDR's, it is to be understood that the methods and systemdisclosed herein may be used with other EDR's presently available oravailable in the future.

As shown in FIG. 14, the process begins at step 710. At step 720, thesystem determines whether the EDR data was obtained from a Type 1, 2, or3 EDR. Next, depending on the determination made at step 720, controlpasses to one of steps 730, 740, or 750 in order to process the dataobtained and engage in a Monte Carlo simulation based on the EDR dataand user input information. This information may include, for example,vehicle pre-impact motion, vehicle property damage and road surfaceconditions.

The results from the Monte Carlo simulation are then returned, and atstep 760, the system determines whether the results are consistent withuser input information and the laws of physics. If the results areconsistent, control passes to step 780 in which the results obtained arereturned to a vehicle accident analysis system, which may use theresults determined from EDR data in connection with analysis of theaccident. Alternately, the results may be simply output to the end userfor analysis and use apart from a separate vehicle accident analysissystem. The method ends at step 790.

Alternately, if it is determined at step 760 that the results are notconsistent, control passes to step 770 to determine whether the userdesires to change the data input and rerun the analysis. If the userdoes not do so, the results determined from EDR data are not used inconnection with analysis of the accident.

FIGS. 15A-C together are a flow chart for an example module 800 toanalyze black box data for a type 1 EDR according to a black boxalgorithm. As shown in FIG. 15A at step 810, the recorded data for atype 1 EDR is obtained. The information obtained from the EDR includesDelta-V, pre-crash speed, pre-crash engine speed, pre-crash percentagethrottle and pre-crash braking. At step 820, the recorded Delta-V isadjusted. In an example embodiment, the Delta-V may be adjusted inaccordance with the discussion of equations 28 and 29 above. However, itis to be understood that in other embodiments, Delta-V need not beadjusted. Also at step 820, the momentum vector component along thevehicle's longitudinal access, P_(x) is calculated using the adjustedDelta-V value, DV′. This momentum vector is calculated according to theequation:P _(x)=mass*DV′  (30)

Next, at step 830, the pre-impact speed is reduced to account forpre-impact braking. If braking occurred at minus one second prior toimpact, constant braking deceleration over a [0,1] second time intervalis used to compute a reduced pre-impact speed. This pre-impact speed maybe calculated according to the equation:V _(reduced) =V−½(f*g)t ²  (31)where V is a vehicle speed at minus one second pre-impact, f is the roadfriction coefficient, g is the gravitational acceleration and t is aperiod of time in the range [0, 1] second.

At step 840, the momentum vector along the vehicle's lateral axis, P_(y)is calculated using the pre-impact speed and the laws of physics. Atstep 850, the momentum vector acting upon the second vehicle isdetermined by using Newton's 3^(rd) law of physics and the accidentconfiguration, as determined by an accident configuration module. Whilenot shown as such in FIG. 15, it is to be understood that the abovesteps may be subjected to a Monte Carlo simulation in which the inputdistributions are randomly sampled and ΔV computed in a loop until thedesired sample size is achieved, or until the maximum number of loopshas been exceeded.

At step 860, consistency checks are made to determine and indicateinconsistencies to the user. In an example embodiment, theseinconsistencies may relate to airbag deployment, seatbelt usage, andaccident severity compared to other methods. Further, because of theinformation provided by a Type 1 EDR, the pre-impact speed of the secondvehicle can be estimated, which can then be compared with the pre-impactmotion of the second vehicle as entered by the user. Thus, at step 860any inconsistencies in these parameters are indicated to the user. Thenat step 870, the user is queried to input whether he wants to change aninput and rerun the analysis. If the user wishes to rerun the analysis,control passes to step 810 in which the method is begun again.Otherwise, control passes to step 875 where it is determined whether theinconsistency violates a law of physics. If the inconsistency does infact violate a law of physics, at step 880 black box data is not used infurther vehicle damage analysis. If the inconsistency does not violate alaw of physics, control passes to step 885 where a warning is displayedto the user. Finally at step 890, a report is generated regarding anyinconsistencies determined by the module. Further, while not shown inFIG. 15, the information determined by the module may be reported toadditional vehicle accident analysis systems for further use. Also, theresults may be simply output to the end user for analysis and use apartfrom a separate vehicle accident analysis system.

FIGS. 16A and B together are a flowchart of an example module foranalyzing a vehicle accident using data from a Type 2 EDR according to ablack box algorithm. As shown in FIG. 16A, module 900 begins at step 910in which the data recorded by the EDR is obtained. As discussed abovethis information includes pre-crash speed, pre-crash engine speed,pre-crash percent throttle and pre-crash braking. At step 915, the useris queried as to whether the other vehicle was stopped, speed known orreasonably bounded prior to impact. If the user answers no, controlpasses to step 920 in which the user is prompted to state whether hedesires to change the input and rerun the analysis. If the user answersyes, control passes to step 910 and the method begins again. Otherwise,control passes step 925, and black box data is not used in furthervehicle accident analysis.

From step 915 if the user indicates that the other vehicle's speed wasknown or reasonably bounded prior to impact, control passes to step 930in which the pre-impact speed is reduced, as discussed above with regardto FIG. 15. At step 935, the closing speed and the laws of physics areused to analyze the accident severity of both vehicles. Then at step 940consistency checks are made. These consistency checks may include airbagdeployment, seat belt usage and accident severity as compared to othermethods. The user is then prompted at step 942 as to whether he desiresto change input values and rerun the analysis. If the user answers yes,control returns to step 910. Otherwise, control passes to step 944 inwhich it is determined whether an inconsistency violates a law ofphysics. If it does, at step 945 black box data is not used in analysisof the vehicle collision. If a law of physics is not violated, controlpasses to step 946 in which a warning message is displayed to the user.Finally, at step 948 a report is generated. As discussed above, whilenot shown in FIG. 16, the information determined may be reported toadditional vehicle accident analysis systems and/or be analyzed and usedapart from such a system. Further, it is to be understood that the abovesteps may be performed iteratively according to a Monte Carlosimulation, as discussed above in connection with FIG. 15.

FIGS. 17A and B together are a flowchart of an exemplary methodaccording to the present invention using black box information from atype 3 EDR in a vehicle accident analysis module according to a blackbox algorithm. As shown in FIG. 17A, method 950 begins at step 955 inwhich data is obtained from the EDR. As discussed above, a Type 3 EDRprovides information regarding Delta-V only. Control then passes to step960 in which the user is queried as to whether the other vehicle wasstopped, speed known or reasonably bounded prior to impact. If the useranswers no, control passes to step 965 in which the user is queriedwhether he desires to change his input and rerun his analysis. If theanswer is yes, control passes back to step 955 to begin the analysisagain. Otherwise, control passes to step 970 in which the black box datais not used for further processing and analysis of the vehicle accident.If, at step 960 the user inputs yes, control passes to step 975 in whichthe recorded Delta-V is adjusted as discussed above with regard to FIG.15. Furthermore, in step 975 the momentum vector component along thevehicle's longitudinal axis P_(x) is calculated as discussed above withrespect FIG. 15. Next, at step 980 the momentum vector along thevehicle's lateral axis, P_(y) is determined using the laws of physics,similar to that discussed above with respect to FIG. 15. Next, at step985 the momentum vector acting upon the second vehicle is determined asdiscussed above with respect to FIG. 15. Finally, at step 990consistency checks are made as discussed above with respect to FIG. 15.Then, while not shown in FIG. 17, the information determined may bereported to additional vehicle accident analysis systems and/or beanalyzed and used apart from such a system. Further, it is to beunderstood that the above steps may be performed iteratively accordingto a Monte Carlo simulation, as discussed above in connection with FIG.15.

While the invention has been described with respect to the embodimentsand variations set forth above, these embodiments and variations areillustrative and the invention is not to be considered limited in scopeto these embodiments and variations. Accordingly, various otherembodiments and modifications and improvements not described herein maybe within the spirit and scope of the present invention, as defined bythe following claims.

1. A computer-implemented method for analyzing a collision between afirst vehicle and a second vehicle, comprising: receiving informationobtained from an event data recorder of said first vehicle that recordsblack box information, at a computer system remote from the firstvehicle and the second vehicle; determining an impact severity of saidfirst and second vehicles in the computer system using said information,the impact severity including a change in velocity for the first andsecond vehicles, by modeling the impact severity in the computer systemusing the Information; and reporting the impact severity from thecomputer system.
 2. The method of claim 1, wherein said informationcomprises a change in velocity of said first vehicle, and furthercomprising calibrating said change in velocity prior to using saidinformation.
 3. The method of claim 1, said receiving comprisingreceiving said information via a network of interconnected computers orvia wireless transmission.
 4. The method of claim 1, further comprisingstoring said information in a secure manner.
 5. The method of claim 1,further comprising comparing said impact severity determined from saidinformation to a second determination of impact severity, said seconddetermination calculated in said computer system without saidinformation.
 6. The method of claim 1, wherein said informationcomprises a change in velocity and a pre-impact speed of said firstvehicle.
 7. The method of claim 1, further comprising using theinformation in the computer system in connection with a database ofvehicle data including benchmark data for a benchmark vehicle related toone of the first or second vehicles to determine the impact severity ofthe one of the first or second vehicles.
 8. The method of claim 1,further comprising using the information in the computer system inconnection with repair estimate information for at least one of thefirst and second vehicles.
 9. The method of claim 1, further comprisingdetermining a point of impact and a principal direction of force foreach of the first and second vehicles.
 10. The method of claim 5,further comprising weighting said impact severity determined from saidinformation differently than the second determination of impactseverity.
 11. The method of claim 6, wherein using said informationcomprises determining a change in velocity of said second vehicle basedon said change in velocity and said pre-impact speed of said firstvehicle.
 12. The method of claim 6, wherein using said informationfurther comprises determining a relative velocity of said first andsecond vehicles based on said change in velocity of said first vehicle.13. The method of claim 9, further comprising determining the impactseverity of at least one of the first and second vehicles using thedetermined point of impact and the determined principal direction offorce.
 14. The method of claim 11, further comprising adjusting saidchange in velocity of said first vehicle prior to using saidinformation.
 15. A computer-implemented method for determining impactseverity of a collision between a first vehicle and a second vehicle,comprising: receiving information obtained from an event data recorderof said first vehicle that records black box data, during a claimevaluation process; electronically storing said information on a storagemedium, said information comprising the black box data; electronicallyaccessing said information from said storage medium; selecting one ofdifferent algorithms to use in modeling said impact severity based on atype of said event data recorder, wherein the selection is further basedon a type or characteristics of information provided by the type ofevent data recorder; utilizing said information in a computer to modelsaid impact severity via the selected algorithm; and reporting theimpact severity from the computer.
 16. The method of claim 15, whereinsaid information comprises a change in velocity of said first vehicle;and wherein said modeling is based at least in part on said change invelocity.
 17. The method of claim 15, further comprising performing asimulation to generate a distribution of change in velocity calculationsfor said first vehicle using said information.
 18. The method of claim17, further comprising determining if said distribution is consistentwith at least one user input.
 19. A computer-implemented method forassessing an accident involving a first vehicle, comprising: receivingblack box information obtained from an event data recorder of said firstvehicle; extracting non-visual data from said black box information;determining a first measure of impact severity by modeling the impactseverity using said non-visual data in a system external to the firstvehicle; determining a second measure of impact severity in the system,the second measure of impact severity determined from informationobtained from a source independent of the first vehicle; weightingdifferently in the system the first measure of impact severity and thesecond measure of impact severity to develop a final measure of impactseverity; and storing the final measure of impact severity in the systemfor later use.
 20. The method of claim 19, wherein said informationcomprises a change in velocity of said first vehicle.
 21. The method ofclaim 19, further comprising selecting an algorithm to use indetermining said first measure of impact severity based on a type ofsaid event data recorder.
 22. The method of claim 19, furthercomprising: receiving input information related to the accident from auser of the system; and determining if at least one inconsistency existsbetween the input information and the first measure of impact severityor the second measure of impact severity.
 23. The method of claim 20,further comprising adjusting said change in velocity before determiningsaid first measure of impact severity.
 24. The method of claim 23,further comprising performing a simulation to generate a distribution ofchange in velocity calculations for said first vehicle using saidadjusted change in velocity.
 25. A computer-implemented method foranalyzing a vehicle accident, comprising: receiving informationharvested from an event data recorder of a first vehicle in a systemexternal to the first vehicle; storing said information in a securemanner in said system; accessing said information in said system;determining a first measure of impact severity for said first vehicle insaid system by modeling based at least in part on said informationharvested from the event data recorder; determining a second measure ofimpact severity in said system for said first vehicle without saidinformation and using information obtained from an independent sourcefrom the event data recorder; and storing the first measure of impactseverity and the second measure of impact severity for later use. 26.The method of claim 25, wherein said information comprises a change invelocity of said first vehicle.
 27. The method of claim 25, furthercomprising weighting the first measure of impact severity and the secondmeasure of impact severity differently.
 28. The method of claim 25,further comprising determining the second measure of impact severity forthe first vehicle using repair estimate information.
 29. The method ofclaim 25, further comprising determining the second measure of impactseverity for the first vehicle using crush information.
 30. The methodof claim 26, further comprising adjusting said change in velocity beforedetermining said first measure of impact severity.
 31. The method ofclaim 26, further comprising determining a pre-impact speed reduction ofsaid first vehicle based at least in part on said information.
 32. Themethod of claim 31, further comprising using the reduced pre-impactspeed in determining said first measure of impact severity.